https://github.com/fatimaafzaal/multiple-ensemble-models-diabetes-prediction-project-

This project focuses on predicting the likelihood of diabetes in individuals using ensemble machine learning models. It combines various ensemble techniques, including Random Forest, AdaBoost, Gradient Boosting, Bagging, Extra Trees, XGBoost, Voting Classifier and some others to get predictions.
https://github.com/fatimaafzaal/multiple-ensemble-models-diabetes-prediction-project-

adaboost catboost colab-notebook diabetes diabetes-prediction ensemble-classifier ensemble-learning ensemble-machine-learning ensemble-model gradient-boosting machine-learning python stacking-classifier voting-classifier xgboost

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This project focuses on predicting the likelihood of diabetes in individuals using ensemble machine learning models. It combines various ensemble techniques, including Random Forest, AdaBoost, Gradient Boosting, Bagging, Extra Trees, XGBoost, Voting Classifier and some others to get predictions.

• Host: GitHub
• URL: https://github.com/fatimaafzaal/multiple-ensemble-models-diabetes-prediction-project-
• Owner: fatimaAfzaal
• Created: 2023-09-24T05:20:15.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-25T13:02:14.000Z (about 1 year ago)
• Last Synced: 2023-09-25T22:48:07.796Z (about 1 year ago)
• Topics: adaboost, catboost, colab-notebook, diabetes, diabetes-prediction, ensemble-classifier, ensemble-learning, ensemble-machine-learning, ensemble-model, gradient-boosting, machine-learning, python, stacking-classifier, voting-classifier, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 114 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# Diabetes Prediction Project Using Ensemble Models

This project is aimed at building a diabetes prediction model using ensemble machine learning techniques. It involves the following steps:

## Problem Definition

The primary goal of this project is to develop a machine learning model that can predict the likelihood of a person having diabetes based on various health-related features. Early detection of diabetes can significantly improve the chances of effective management and treatment.

## Data Collection and Exploration

In this step, we collected our dataset from Kaggle, which contains various health-related parameters of individuals. We explored the dataset to gain insights into the data and used visualizations to better understand the data distribution.

## Data Preprocessing

Data preprocessing is a crucial step to handle missing values and encode categorical data. We also performed correlation analysis to identify important features for our prediction model.

## Model Selection and Training

We experimented with several ensemble machine learning algorithms to build our prediction model. These algorithms include:

- Random Forest Classifier

- AdaBoost Classifier

- Gradient Boosting Classifier

- Bagging Classifier

- Extra Trees Classifier

- XGBoost Classifier

- Voting Classifier

- Stacking Classifier

- CatBoost Classifier

- Passive Aggressive Classifier

We trained each of these models and evaluated their performance using accuracy metrics.

## Model Evaluation

We assessed the accuracy of each model using the testing dataset and selected the best-performing model for our diabetes prediction task.

## User Input and Prediction

In the final section of the code, users can input health-related parameters, and the trained model will predict whether the individual is likely to have diabetes or not.